
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "160"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 110 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 105 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	25.41%	0.312579	-0.852205	3.203520	0.727554	0.994681	0.507662	2.115006	0.739769	0.7820	0.7960	0.2441	3.564045	3.286929
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.15%	3.23%	1.046261	-0.390510	3.791451	1.500609	1.869507	1.284020	3.581576	0.225861	0.7446	0.7455	0.2494	2.398173	2.340303
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.021317	-0.936618	0.406210	-0.428112	0.846129	0.000142	1.108258	-0.041703	0.0420	0.0329	0.0005	nan	nan
2459591	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.045331	-0.481776	4.452259	1.411859	0.787941	1.598620	-0.783667	-0.384735	0.6735	0.6914	0.3644	5.246412	4.446966
2459590	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.573297	-0.196089	3.955836	0.866935	0.985922	1.810788	2.695345	-0.292809	0.6734	0.6879	0.3571	6.266543	5.737740
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	20.43%	-0.134064	-0.525558	2.921890	0.377143	0.752123	0.067665	1.224032	0.021853	0.7103	0.7112	0.2833	1.867044	1.890745
2459588	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.887958	-0.810293	4.766911	1.540346	0.467577	1.176071	2.059270	0.027434	0.7856	0.7776	0.2151	10.131133	7.811253
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	351.754851	354.478890	inf	inf	38.401837	102.847061	275.432028	512.757971	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	7.03%	0.00%	-0.200364	-0.817886	-0.880946	-0.352871	-1.156894	1.301440	0.536339	-0.478779	0.6526	0.6699	0.3611	1.641791	1.624121
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.370064	0.837210	-0.225860	1.748476	1.482618	2.837655	0.848394	0.717942	0.0382	0.0428	0.0030	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.679551	0.279319	6.296145	-0.421863	9.681608	-0.191986	4.766734	1.981042	0.6156	0.6279	0.3597	0.000000	0.000000
2459583	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.536739	-0.309300	2.659081	-0.695222	0.230126	1.284899	4.541793	0.907173	0.6743	0.6906	0.3771	4.284896	4.060691
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	2.70%	0.00%	0.896569	-0.206472	2.638118	-0.484745	2.161439	-0.222577	0.421511	0.067768	0.6760	0.6896	0.3786	1.469299	1.477544
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	3.24%	0.00%	1.371359	-0.188210	2.008255	-0.175127	1.346312	-0.189129	1.278155	-0.473409	0.6832	0.6957	0.3722	1.797217	1.689546
2459580	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.599412	-0.321823	2.956419	-0.091232	1.534998	0.418526	8.957802	0.616587	0.6754	0.6892	0.3745	4.745734	4.239528
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.742444	-0.461139	3.659395	-0.189748	1.109887	0.559826	0.032729	-0.185540	0.6718	0.6854	0.3757	0.000000	0.000000
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.639617	0.481559	0.315475	-0.157156	2.655962	-0.017497	2.596176	-0.667021	0.0474	0.0443	0.0023	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	16.22%	0.583060	-0.099793	1.905436	-0.243418	1.538641	-0.054581	3.498079	-0.552753	0.6725	0.6847	0.3772	1.632478	1.514300
2459576	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459575	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	0.00%	0.031792	-0.427017	1.605776	-0.718383	0.345177	-0.462024	1.363337	-0.718987	0.7543	0.7630	0.3220	1.660197	1.584688
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.510404	-0.441624	1.162545	-0.668704	0.868626	-1.301824	3.922191	-0.407019	0.6945	0.7070	0.3737	1.648156	1.534911
2459573	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.630692	0.839547	3.770606	-0.203178	0.917576	-0.000669	-0.078765	0.000783	0.6954	0.7062	0.3726	1.524582	1.524602
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.953780	0.731410	1.502877	-0.423962	0.876019	-0.627788	-0.949221	-0.311819	0.6998	0.7106	0.3685	1.550476	1.579836
2459571	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	nan	nan
2459570	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.54%	-0.516210	-0.685104	-0.382008	-0.706475	-0.426255	-0.642635	-0.586639	-0.729019	0.6837	0.7006	0.3465	1.118534	1.134945
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	0.243683	-0.084479	1.782686	-0.533319	1.537112	-0.788382	1.164603	-0.678206	0.7660	0.7733	0.2352	2.113484	1.826892
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	0.458924	-0.918451	2.410356	-0.389011	0.332932	0.301469	-0.030383	-0.608507	0.6872	0.7046	0.3919	1.538291	1.529638
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	-2.008718	-2.008718	-1.878795	-1.878795	-0.888145	-0.888145	-0.944419	-0.944419	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	0.00%	-0.672328	-0.434364	-2.543705	0.097592	0.597776	1.210468	1.876393	-0.001373	0.7023	0.7114	0.3855	1.540172	1.470878
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.342530	-0.859455	2.336998	0.363287	0.103915	0.276039	2.212244	-0.033021	0.6994	0.7121	0.3825	1.779686	1.613183
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.963275	-0.611659	-1.969856	-0.565878	-0.885388	0.289647	1.175740	-0.018484	0.7003	0.7163	0.3791	1.855066	1.590630
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.910911	-0.554143	-2.221976	-0.598573	-1.400151	1.037684	0.313208	-0.250333	0.7007	0.7179	0.3785	1.818046	1.597868
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.627745	-0.562711	-1.923083	-0.672876	-1.273672	1.092306	0.820666	0.018194	0.6984	0.7157	0.3873	1.720145	1.530092
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-1.158277	-0.970280	-2.054655	-0.907973	-1.692782	0.685133	0.618245	-0.219188	0.6953	0.7101	0.3798	1.797913	1.548945
2459558	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.097887	-0.908040	2.970932	-0.415418	0.867297	0.843217	7.140223	1.649111	0.6987	0.7090	0.3791	3.530879	3.104797
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0484	0.0375	0.0009	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.54%	1.084986	-0.843430	3.340075	-0.543852	2.091317	0.804743	1.395347	-0.014814	0.6932	0.7067	0.3827	1.730685	1.551916
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7025	0.7173	0.3884	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6970	0.7108	0.3867	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.039503	-0.571472	4.236833	0.696416	6.174540	1.276894	1.218449	-0.355803	0.7085	0.7171	0.3822	10.558333	8.521189
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.348690	-0.683535	-2.319218	-0.951913	-2.111500	0.956746	0.854739	-0.094389	0.7019	0.7161	0.3815	1.722023	1.543642
2459550	digital_ok	-	97.53%	97.53%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0579	0.0556	0.0012	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	8.11%	0.00%	0.331912	-0.740068	2.790229	-0.279344	1.731650	0.735138	1.527485	-0.436977	0.6949	0.7075	0.3967	1.763406	1.561660
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	0.445910	-0.689724	3.419589	0.402168	0.878499	-0.151573	3.217952	-0.312664	0.7466	0.7501	0.3540	1.796495	1.554152
2459541	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	0.00%	-0.431427	-0.483653	-1.692099	-0.573314	-2.163379	0.049349	2.265383	0.045936	0.7527	0.7563	0.3383	1.590154	1.480570
2459536	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.742496	-0.252424	5.273960	1.311242	4.548265	-0.309835	24.860523	1.009287	0.7284	0.7348	0.4071	3.679547	3.013759
2459535	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.341264	-0.609565	-1.808128	-0.527314	-2.357819	-0.632334	8.159926	0.394555	0.7890	0.7806	0.3514	4.054482	3.316293
2459534	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.932049	-0.695565	-1.977598	-0.467808	-0.835505	-0.248962	9.683411	0.950684	0.7801	0.7679	0.3450	4.560150	3.588457
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	0.00%	0.809314	-0.566858	3.416184	-0.163682	2.960087	0.266695	-0.668887	0.677212	0.7190	0.7290	0.3952	1.530640	1.444519
2459532	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.071965	-0.804224	4.547449	-0.129418	3.395909	-0.309707	1.274635	1.091269	0.7252	0.7356	0.3893	3.793286	3.327499
2459530	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.825846	-0.511177	-1.843484	-0.612909	-1.746659	0.527988	7.489119	0.717886	0.7319	0.7353	0.3925	4.688718	3.706526
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.165863	0.697302	2.970743	-0.724515	3.056932	0.551017	1.888242	-0.232409	0.9090	0.9357	0.3014	0.000000	0.000000
2459523	digital_ok	0.00%	-	-	-	18.37%	0.00%	0.009068	3.716677	3.921516	0.111008	0.710317	0.573462	0.597444	-0.477532	nan	nan	nan	2.242178	1.972029
2459522	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.533528	2.738588	4.314631	0.193803	0.751030	-0.263758	9.374641	0.354222	0.6653	0.6721	0.3507	3.265445	3.655193
2459513	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	1.005183	0.468163	0.073181	-0.628227	0.763107	0.089660	3.711661	0.329920	0.0554	0.0609	0.0076	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	0.579271	-0.959269	2.451071	-1.128482	1.121396	-0.572549	1.541259	-0.801094	0.9122	0.9435	0.1883	0.000000	0.000000
2459505	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.258469	-1.222508	3.553213	-0.218365	0.139474	0.330625	11.694456	0.754518	0.8896	0.8543	0.2093	26.775970	19.916951
2459503	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.741057	-0.729579	3.573061	0.205561	5.652355	0.907112	3.845243	0.094397	0.8846	0.8779	0.4332	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 160 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 160, Node 13:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 160: 2459634

Antenna 160: 2459632

Antenna 160: 2459631

Antenna 160: 2459630

Antenna 160: 2459629

Antenna 160: 2459628

Antenna 160: 2459627

Antenna 160: 2459626

Antenna 160: 2459625

Antenna 160: 2459624

Antenna 160: 2459623

Antenna 160: 2459622

Antenna 160: 2459621

Antenna 160: 2459620

Antenna 160: 2459619

Antenna 160: 2459618

Antenna 160: 2459617

Antenna 160: 2459616

Antenna 160: 2459615

Antenna 160: 2459614

Antenna 160: 2459613

Antenna 160: 2459612

Antenna 160: 2459611

Antenna 160: 2459610

Antenna 160: 2459609

Antenna 160: 2459608

Antenna 160: 2459607

Antenna 160: 2459605

Antenna 160: 2459604

Antenna 160: 2459603

Antenna 160: 2459602

Antenna 160: 2459598

Antenna 160: 2459597

Antenna 160: 2459596

Antenna 160: 2459595

Antenna 160: 2459594

Antenna 160: 2459593

Antenna 160: 2459592

Antenna 160: 2459591

Antenna 160: 2459590

Antenna 160: 2459589

Antenna 160: 2459588

Antenna 160: 2459587

Antenna 160: 2459586

Antenna 160: 2459585

Antenna 160: 2459584

Antenna 160: 2459583

Antenna 160: 2459582

Antenna 160: 2459581

Antenna 160: 2459580

Antenna 160: 2459579

Antenna 160: 2459578

Antenna 160: 2459577

Antenna 160: 2459576

Antenna 160: 2459575

Antenna 160: 2459574

Antenna 160: 2459573

Antenna 160: 2459572

Antenna 160: 2459571

Antenna 160: 2459570

Antenna 160: 2459569

Antenna 160: 2459566

Antenna 160: 2459565

Antenna 160: 2459564

Antenna 160: 2459563

Antenna 160: 2459562

Antenna 160: 2459561

Antenna 160: 2459560

Antenna 160: 2459559

Antenna 160: 2459558

Antenna 160: 2459556

Antenna 160: 2459552

Antenna 160: 2459551

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶